¹³¹I whole-body scintigraphy (WBS) is a highly sensitive method for the detection of differentiated thyroid tumors and metastases. However, a lack of anatomic landmarks and the physiologic accumulation of the tracer complicate interpretation of the images. This prospective study was designed to evaluate the incremental value of ¹³¹I SPECT/CT over planar WBS in the management of patients with differentiated thyroid carcinoma (DTC). Methods: Planar imaging was performed on 66 consecutive DTC patients who were considered to have locally advanced or metastatic disease after total or nearly total thyroidectomy. SPECT/CT was added for patients whose planar findings were inconclusive. The planar images were interpreted by 2 experienced nuclear medicine physicians. Interpretation of the SPECT/CT images was a consensus opinion of one of the nuclear medicine physicians and an experienced radiologist. Fusion images were considered to improve image interpretation when they better localized sites of increased ¹³¹I uptake. The final diagnosis was verified by pathologic findings, other imaging modalities, and clinical follow-up. Both site-based and patient-based analyses were performed, and the impact of SPECT/CT results on therapeutic strategy was assessed. Results: A total of 232 foci were observed by ¹³¹I WBS, including 33.2% of foci localized in the thyroid bed, 62.1% due to malignant lesions, and 4.7% caused by nonthyroidal physiologic or benign uptake or a contaminant. Overall, 37 SPECT/CT studies were performed on 23 patients, whose planar images showed 81 inconclusive lesions. Precise localization and characterization of ¹³¹I-avid foci were achieved through ¹³¹I SPECT/CT in 69 (85.2%) and 67 (82.7%) of the 81 foci, respectively. Fusion images were considered to be of benefit in 17 (73.9%) of 23 patients. The therapeutic strategy was changed in 8 (47.1%) of 17 patients. Uncommon metastatic lesions were found in 9 (13.6%) of 66 patients with regard to SPECT/CT fusion images. Conclusion: Fusion of SPECT and CT images was of incremental value over WBS in increasing diagnostic accuracy, reducing pitfalls, and modifying therapeutic strategies in 73.9% of DTC patients. As SPECT/CT techniques emerge, ¹³¹I SPECT/CT may demonstrate higher value than WBS in the management of DTC.

Poorly differentiated thyroid carcinoma (PDTC) is a rare thyroid carcinoma originating from follicular epithelial cells. No explicit consensus can be achieved to date due to sparse clinical data, potentially compromising the outcomes of patients. In this comprehensive review from a clinician’s perspective, the epidemiology and prognosis are described, diagnosis based on manifestations, pathology, and medical imaging are discussed, and both traditional and emerging therapeutics are addressed as well. Turin consensus remains the mainstay diagnostic criteria for PDTC, and individualized assessments are decisive for treatment option. The prognosis is optimal if complete resection is performed at early stage but dismal in nearly half of patients with locally advanced and/or distant metastatic diseases, in which adjuvant therapies such as ¹³¹I therapy, external beam radiation therapy, and chemotherapy should be incorporated. Emerging therapeutics including molecular targeted therapy, differentiation therapy, and immunotherapy deserve further investigations to improve the prognosis of PDTC patients with advanced disease.

HER inhibitor promotes BRAF/MEK inhibitor-induced redifferentiation in papillary thyroid cancer harboring BRAF V600E

Clinical efficacy of redifferentiation therapy with histone deacetylase inhibitor (HDACi) for lethal radioiodine-refractory papillary thyroid cancer (RR-PTC) is urgently needed to be improved. Given that the impairment of histone acetylation is a mechanism in BRAF^V600E-mitogen-activated protein kinase (MAPK)-induced aberrant silencing of thyroid iodine-metabolizing genes, dual inhibition of HDAC and MAPK may produce a more favorable effect. In this study, we treated BRAF^V600E-mutant (BCPAP and K1) and BRAF-wild-type (BHP 2-7) cells with HDACi (panobinostat) and MAPK inhibitor (dabrafenib or selumetinib), alone or in combination, and we tested the expression of iodine- and glucose-metabolizing genes, radioiodine uptake and efflux, and toxicity. We found that panobinostat alone increased iodine-metabolizing gene expression, promoted radioiodine uptake and toxicity, and suppressed GLUT1 expression in all the cells. However, MAPKi (dabrafenib or selumetinib) induced these effects only in BRAF^V600E-mutant cells. Combined treatment with panobinostat and MAPKi (dabrafenib or selumetinib) displayed a more robust BRAF^V600E-dependent redifferentiation effect than panobinostat alone via further improving the acetylation level of histone at the sodium-iodide symporter (NIS) promoter. In conclusion, MAPK inhibitors enhance HDACi-induced redifferentiation in PTC cells harboring BRAF^V600E, warranting animal and clinical trials.

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019–2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019–2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan–Meier survival curves. Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95–0.99) for internal validation. External validation further confirmed the RF model’s effectiveness, yielding an AUC of 0.97 (95% CI 0.94–0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. This work is supported by the 10.13039/501100001809 National Natural Science Foundation of China (NO.81974315); 10.13039/501100003399 Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).